agentleFS
wrg32786wrg32786/aigent-os/skills/compact-note/SKILL.md

compact-note

Skill25 starsChanged 7 days agoMITView on GitHub

Compact vault notes into standing rules and a shortened log. Use when a note is bloated, a log needs trimming, or memory needs distilling.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 4 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read the entire note. Do not compact from partial context.

    line 31
  2. Very specific:

    Check frontmatter for needs_compact: true — if present, this is a scheduled pass.

    line 33
  3. Very specific:

    Add compaction_ratio: X:1 (original lines / compacted lines)

    line 63
  4. Distinctive:

    Read the full note before compacting.

    line 79
  5. Distinctive:

    Never compact so aggressively that the rationale for a decision is lost.

    line 80
  6. Distinctive:

    If in doubt about an entry's load-bearing status, preserve it and flag it.

    line 83

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Compact Note

You are Mnemosyne compacting a bloated vault note. Distill signal into standing rules. Truncate raw log. Never compact so aggressively that rationale is lost.

What this skill does

Take a long-running, high-churn vault note (session log, decision log, feedback ledger, tracker) and compact it: identify recurring patterns as standing rules, truncate the raw log to a summary, preserve load-bearing entries, and return a compacted note with a clear before/after ratio.

Protocol

Step 1: Read the full note

Read the entire note. Do not compact from partial context.

Check frontmatter for needs_compact: true — if present, this is a scheduled pass.

Step 2: Classify all entries

Walk each entry and classify:

  • Standing rule candidate — a pattern that has repeated 3+ times
  • Load-bearing — an entry explaining why a decision was made; must be preserved
  • Resolved — outcome is known and the entry no longer affects future decisions; can be truncated
  • Stale — context has shifted, entry no longer applies; strike it

Step 3: Compact

Structure the compacted note:

## Standing Rules (distilled from log)
<Rule 1 — drawn from entries X, Y, Z>
<Rule 2 — ...>

## Active Threads (load-bearing, not yet resolved)
<Thread A — last updated YYYY-MM-DD>

## Archive (truncated log — full history in git)
<Summarized in 1-2 bullets per month or sprint>
YYYYMM: <N entries> — <one-line summary of what happened>

Update frontmatter:

  • Set needs_compact: false
  • Add last_compacted: YYYY-MM-DD
  • Add compaction_ratio: X:1 (original lines / compacted lines)

Step 4: Return honesty ledger

## Honesty Ledger

**Notes touched:** <list>
**Entries superseded:** <count>
**Entries preserved:** <count — with reason for each load-bearing entry>
**Compaction ratio:** X:1 (original N lines → compacted N lines)
**Residual uncertainty:** <entries that were ambiguous to classify>

Constraints

  • Read the full note before compacting. No partial-context edits.
  • Never compact so aggressively that the rationale for a decision is lost.
  • Standing rules must be extractable from 3+ repeated patterns — do not invent rules.
  • Load-bearing entries (the "why" behind decisions) survive compaction.
  • If in doubt about an entry's load-bearing status, preserve it and flag it.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.